Metadata-Version: 1.1
Name: cpbd
Version: 1.0.7
Summary: Calculate the sharpness of an image with the CPBD metric
Home-page: https://github.com/0x64746b/python-cpbd
Author: D.
Author-email: dtk@gmx.de
License: Other/Proprietary License
Description-Content-Type: UNKNOWN
Description: About
        =====
        
        CPBD is a perceptual-based no-reference objective image sharpness metric
        based on the cumulative probability of blur detection `developed at the
        Image, Video and Usability Laboratory of Arizona State
        University <https://ivulab.asu.edu/Quality/CPBD>`__.
        
            [The metric] is based on the study of human blur perception for
            varying contrast values. The metric utilizes a probabilistic model
            to estimate the probability of detecting blur at each edge in the
            image, and then the information is pooled by computing the
            cumulative probability of blur detection (CPBD).
        
        This software is a Python port of the `reference MATLAB
        implementation <http://lina.faculty.asu.edu/Software/CPBDM/CPBDM_Release_v1.0.zip>`__.
        To approximate the behaviour of MATLAB's proprietary implementation of
        the Sobel operator, it uses an implementation `inspired by GNU
        Octave <https://sourceforge.net/p/octave/image/ci/default/tree/inst/edge.m#l196>`__.
        
        References
        ==========
        
        CPBD is described in detail in the following papers:
        
        -  `N. D. Narvekar and L. J. Karam, "A No-Reference Image Blur Metric
           Based on the Cumulative Probability of Blur Detection (CPBD)," in
           IEEE Transactions on Image Processing, vol. 20, no. 9, pp. 2678-2683,
           Sept.
           2011. <http://ieeexplore.ieee.org/abstract/document/5739529/>`__
        -  `N. D. Narvekar and L. J. Karam, "An Improved No-Reference Sharpness
           Metric Based on the Probability of Blur Detection," International
           Workshop on Video Processing and Quality Metrics for Consumer
           Electronics (VPQM), January 2010, http://www.vpqm.org
           (pdf) <http://events.engineering.asu.edu/vpqm/vpqm10/Proceedings_VPQM2010/vpqm_p27.pdf>`__
        -  `N. D. Narvekar and L. J. Karam, "A no-reference perceptual image
           sharpness metric based on a cumulative probability of blur
           detection," 2009 International Workshop on Quality of Multimedia
           Experience, San Diego, CA, 2009, pp.
           87-91. <http://ieeexplore.ieee.org/abstract/document/5246972/>`__
        
        Credits
        =======
        
        If you publish research results using this code, I kindly ask you to
        reference the papers of the original authors of the metric as stated in
        the previous section as well as their reference implementation in your
        bibliography. See also the copyright statement of the reference
        implementation in the `license
        file <https://raw.githubusercontent.com/0x64746b/python-cpbd/master/LICENSE.txt>`__.
        Thank you!
        
        Installation
        ============
        
        ::
        
            $ pip install cpbd
        
        Usage
        =====
        
        ::
        
            In [1]: import cpbd
        
            In [2]: from scipy import ndimage
        
            In [3]: input_image = ndimage.imread('/tmp/LIVE_Images_GBlur/img4.bmp', mode='L')
        
            In [4]: cpbd.compute(input_image)
            Out[4]: 0.75343203230148048
        
        Development
        ===========
        
        ::
        
            $ git clone git@github.com:0x64746b/python-cpbd.git
            Cloning into 'python-cpbd'...
            $ cd python-cpbd
            $ pip install -U '.[dev]'
        
        To quickly run the tests with the invocation interpreter:
        
        ::
        
            $ python setup.py test
        
        To test the library under different interpreters:
        
        ::
        
            $ tox
        
        Performance
        ===========
        
        The following graph visualizes the accuracy of this port in comparison
        with the reference implementation when tested on the
        `images <http://lina.faculty.asu.edu/Software/CPBDM/LIVE_Images_GBlur.zip>`__
        of the `LIVE
        database <http://live.ece.utexas.edu/research/quality/subjective.htm>`__:
        
        .. image:: https://raw.githubusercontent.com/0x64746b/python-cpbd/master/tests/data/performance_LIVE.png
           :alt: Performance on LIVE database
        
Keywords: sharpness,metric,blur,cumulative probability,no-reference,objective,perceptual
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: License :: Other/Proprietary License
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
